The following text deals with image analysis methods, in particular with methods for segmentation of (parts of) objects in an image, for example a computer tomographic image of a joint within the human or animal body (like hip joints, shoulder joints, vertebral joints, knee joints, wrist joints, etc.) wherein the objects connected via the joint can be segmented. For image processing, a segmentation (i.e. a separation) of different parts of different, or same, objects may be necessary. The parts may have different properties in the image, i.e. variations in features such as gray scale/brightness, color and similar. These differences result from distinct properties of the imaged objects. Depending on the imaging process, properties such as density, material composition, water content, content of a tracer, absorbency/reflection rate for ultrasonic waves or light and similar can be distinguished. The task of segmentation of the representations of parts of the real objects, which involves segregating regions with the same or similar properties in the image and building coherent groups, can be automatically accomplished by means of algorithms. Especially in medical imaging, it is desirable to automatically accomplish the segmentation of different forms of tissue, such as fat, muscle and bone, and the recognition of coherent regions (e.g. bones or organs) in the course of the imaging process or image processing. With particular regard to maxillofacial surgery and dental treatments, an algorithm to automatically separate the condyle head from the skull base (more specifically, the fossa) at the temporomandibular joint would be very useful.
In medical image processing, correlation techniques are used to facilitate an automatic identification of regions in an image representing certain parts or structures such as organs, bones, joints or, respectively, parts thereof. Those regions are called Regions of Interest (ROI). A correlation technique uses templates of structures that are matched with the image data in order to localize the ROI. For that purpose, similarity measures are computed with a cross-correlation or a normalized cross-correlation representing the grade of correlation (or similarity) between the template and different regions in the image data. The region with the best similarity measure is selected for the further image processing steps. Such correlation techniques are generally known in the art.
In order to assess the information contained in an image, the different parts and/or objects shown in the image have to be unambiguously distinguishable. In images derived from image data, some parts of objects or objects as such may appear to be integrally connected, although there is no such connection in reality. This kind of erroneous fusion can arise during data capturing, the reconstruction of the image and/or image processing steps. The segmentation of such fused elements is hard to automate in current image processing systems and, therefore, frequently requires delineation by hand from the user. Manual segmentation of objects or parts thereof can be very difficult when the latters are small and the gaps in between are narrow. Also, in three dimensions, the user's view is typically obstructed by other objects or parts. Thus, a user might choose wrong lines and surfaces. In addition, segmentation by hand is labor-intensive and can be very time consuming, thus leading to high costs.
The mandible (i.e. the lower jaw) is a bony structure that is connected to the skull base by two temporomandibular joints (TMJ), residing on top of the left and right condyle. These joints allow the mandible to move, e.g. for chewing. Due to their low density in computed tomography (CT) images, they define a 3D channel in between the denser bone of condyle and fossa.
A maxillofacial surgeon using surgical planning software should be able to virtually reposition the mandible of a patient in order to obtain a better occlusion or to mimic realistic jaw articulation. To this end, separate virtual surface objects are needed: one representing the mandible and the other representing the rest of the skull. Specifically, the surface representation of the condyle at the apex of the mandible must be fully separated from the skull base.
Presently, this is usually not the case because the gray values in the TMJ region can be quite fuzzy due to a variety of reasons. When some TMJ voxels have a gray value larger than the isovalue threshold, which is chosen by the user to mark the visible surface in an image resulting from DICOM image data, the surfaces get connected. Limited scanner accuracy, noise, patient movement and/or the partial volume effect (PVE) are the most common causes. PVE occurs when the CT scanner beam is non-linearly attenuated by two different types of tissue that occur together in a voxel. Streak artifacts can cause a virtual channel obstruction by introducing spurious large gray values inside the channel.
Non-standard patient anatomy or TMJ defects, like partial ankylosis or joint resorption, can even result in physical contact between the joint and fossa. As such, the low-density channel defined by the TMJ is partially blocked and the virtual surface models will be connected. It is understood that this prevents the surgeon from virtually repositioning the mandible.